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Computational Science and Engineering in Electrochemical Energy Systems

Kyle Smith, University of Illinois

Partha Mukherjee, Purdue University

The functionality of electrochemical energy systems is determined by the electronic, ionic, fluidic, thermal, mechanical, and material transport processes occurring in electrodes and membranes.  As such, the continuum modeling of such processes plays an important role in the design, development, control, and characterization of devices for energy storage, energy conversion, environmental, and sensing devices.  The transport processes in electrochemical systems occur in a concerted manner, requiring models that explicitly couple such processes across scales and between materials.  In the porous electrodes commonly used in rechargeable batteries, for example, macro-homogeneous formulations are readily used by volume-averaging conservation equations at macroscopic scales.  Alternatively, pore-scale formulations explicitly resolve transport within the microscopic distribution of solid and liquid constituents of electrodes.  Furthermore, the lifetime of electrochemical devices is affected by chemical and mechanical degradative processes, the modeling of which can be used to develop new systems and to interpret experimental data.  Accordingly, this mini-symposium will focus on the modeling of transport processes and mechanics in electrochemical devices and materials.  Both fundamental- and application-based research is appropriate.  Systems of interest will include rechargeable Li-ion batteries and beyond, redox flow batteries, and fuel cells.  Research that focuses on the physics-based modeling of coupled transport processes and mechanics across scales is of particular interest.  Modeling topics of interest include, but are not limited to, multi-component transport phenomena, microstructure-resolved transport processes and degradation, convective transport phenomena within porous electrodes, multi-phase transport, material evolution, chemo-mechanics, non-isothermal phenomena, uncertainty quantification, optimization, and efficient numerical methods for all of the above.